StefonSimmons commented 4 days ago

What does this PR do?

removes kind frontmatter from pages ingested by docs site:

Motivation

the kind param is no longer a user-defined frontmatter in newer versions of Hugo. it is built-in to be one of home, page, section etc.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

agent-platform-auto-pr[bot] commented 4 days ago

[Fast Unit Tests Report]

On pipeline 38079233 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

- tests_deb-arm64-py3 - tests_deb-x64-py3 - tests_flavor_dogstatsd_deb-x64 - tests_flavor_heroku_deb-x64 - tests_flavor_iot_deb-x64 - tests_rpm-arm64-py3 - tests_rpm-x64-py3 - tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

pr-commenter[bot] commented 4 days ago

Regression Detector

Regression Detector Results

Run ID: 77fd6f20-9cea-4bc1-8e0a-0b1da51257c9 Metrics dashboard Target profiles

Baseline: ff3269e00c93678d2a3dd7df167bd272a6071fcf Comparison: 153b941dff8be79b5cefeff786c7bcbcca85dc13

Performance changes are noted in the perf column of each table:

✅ = significantly better comparison variant performance
❌ = significantly worse comparison variant performance
➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

| perf | experiment | goal | Δ mean % | Δ mean % CI | links | |------|----------------------------|--------------------|----------|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +2.67 | [-2.22, +7.56] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +1.42 | [+0.53, +2.31] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) | | ➖ | file_tree | memory utilization | +0.19 | [+0.14, +0.24] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | +0.01 | [-0.04, +0.05] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) | | ➖ | idle | memory utilization | -0.16 | [-0.20, -0.12] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.52 | [-1.33, +0.29] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) | | ➖ | basic_py_check | % cpu utilization | -1.81 | [-4.44, +0.82] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -4.62 | [-17.23, +7.99] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A77fd6f20-9cea-4bc1-8e0a-0b1da51257c9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719877571000&to_ts=1719888971000&live=false) |

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI". For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true: 1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look. 2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that *if our statistical model is accurate*, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants. 3. Its configuration does not mark it "erratic".

paulcacheux commented 3 days ago

/merge

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